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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Unsupervised compositionality prediction of nominal compounds Cordeiro, S.; Villavicencio, A.; Idiart, M.. - : MIT Press - Journals, 2019 BASE Show details 2 A corpus study of verbal multiword expressions in Brazilian Portuguese Ramisch, C.; Ramisch, R.; Zilio, L.. - : Springer International Publishing, 2018 BASE Show details 3 UFRGS&LIF at SemEval-2016 task 10: Rule-based MWE identification and predominant-supersense tagging Cordeiro, S.R.; Ramisch, C.; Villavicencio, A.. - : Association for Computational Linguistics, 2016 BASE Show details 4 How naked is the naked truth? A multilingual lexicon of nominal compound compositionality Villavicencio, A.; Wilkens, R.; Ramisch, C.. - : Association for Computational Linguistics, 2016 BASE Show details 5 Filtering and measuring the intrinsic quality of human compositionality judgments Ramisch, C.; Cordeiro, S.; Villavicencio, A.. - : Association for Computational Linguistics, 2016 BASE Show details 6 mwetoolkit+sem: Integrating word embeddings in the mwetoolkit for semantic MWE processing Cordeiro, S.; Ramisch, C.; Villavicencio, A.. - : European Language Resources Association (ELRA), 2016 Abstract: This paper presents mwetoolkit+sem: an extension of the mwetoolkit that estimates semantic compositionality scores for multiword expressions (MWEs) based on word embeddings. First, we describe our implementation of vector-space operations working on distributional vectors. The compositionality score is based on the cosine distance between the MWE vector and the composition of the vectors of its member words. Our generic system can handle several types of word embeddings and MWE lists, and may combine individual word representations using several composition techniques. We evaluate our implementation on a dataset of 1042 English noun compounds (Farahmand et al., 2015), comparing different configurations of the underlying word embeddings and word-composition models. We show that our vector-based scores model non-compositionality better than standard association measures such as log-likelihood. URL: http://eprints.whiterose.ac.uk/153563/ https://www.aclweb.org/anthology/L16-1194 BASE Hide details 7 Predicting the compositionality of nominal compounds: Giving word embeddings a hard time Cordeiro, S.; Ramisch, C.; Idiart, M.. - : Association for Computational Linguistics, 2016 BASE Show details 8 Comparing the quality of focused crawlers and of the translation resources obtained from them Laranjeira, B.R.; Moreira, V.P.; Villavicencio, A.. - : European Language Resources Association (ELRA), 2014 BASE Show details 9 Nothing like good old frequency: Studying context filters for distributional thesauri Padró, M.; Idiart, M.; Ramisch, C.. - : Association for Computational Linguistics, 2014 BASE Show details

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